Note di Matteo

Testimonianze che stanno girando su Twitter che frenano un po' il trend forzato che sta trasformando lo sviluppo software in pura velocità e quantità rimuovendo l'ingegneria:

I am done with this shit. It is over. The state of engineering right now is horrible. It has been half a month since I started a new role at a big company. Nobody knows anything here. The specs, code, tests, PRDs, tickets, resolution of those tickets, reports, etc., everything is made by Claude Code. Nobody on my team likes this. They are being forced to ship as much as they can. I have heard multiple times from higher management that pushing code is not a bottleneck, so why are we slow? People are working 12 to 13 hours a day just to press enter. Nobody is reading anything. Humans in corporate are doing nothing on their own. Everyone, literally everyone, from an L1 to an L7 engineer here is doing the same thing. Talk to Claude. There is no sense of victory. Nobody is resolving bugs. In reality, nobody is thinking anymore. Everything is done by LLMs. It is so soul-sucking. I would not mind it, to be honest, if we were at least given the time to check out the code and see what is going where. But no, the goal is to just ship. No matter what happens.

(@v0xium)

I’m slowly starting to hate AI code review.

Massive amounts of nitpicking and an ungodly amount of corner-case defensive programming. After a few rounds the intent of the code is lost under mountains of extra code forced by the reviewer.

I’m starting to be more agressive with implementer agents pushing back heavily for the reviewer agents to prove why their nitpick will make the code better, only for the reviewers to backtrack in frenzy. SO WHY DID YOU RAISE THIS IN THE FIRST PLACE?

All this because I still require that „this code must be readable and easily understandable”

(@marcinbunsch)

Ora che i poster di feste e sagre sono tutti identici perché fatti con ChatGPT o Gemini, un tizio ha testato per noi come generare stili più unici con ChatGPT. Qua c'è una galleria di prompt (in inglese) per diversi stili:

A OpenAI gli ingegneri non sono più specializzati in tecnologie specifiche ma contano di più queste qualità:

• Judgment: is an approach the right one, or is another more optimal? Should a feature be shipped or binned?
• Prioritization: what’s the next most important thing to work on and what is not worth doing, right now?
• Agency: the best engineers relentlessly come up with ideas and parallelize with agents as much as possible without being prompted.
• Taste: what does “great” actually look like? Engineers with good taste produce standout artifacts in functionality, architecture, design, etc.

The better these tools become at resolving routine incidents, the less practice human responders will get. And when an ambiguous, high-severity incident comes in that automation cannot solve, responding engineers will be in trouble.

These AI-assisted incident response tools, more commonly called “AI SREs” – a term I don’t particularly like – are fantastic in many ways. They feel especially magical when they handle a routine incident at night and you don’t have to wake up for a capacity issue.

The problem is that routine incidents are also how responders “safely” develop an intuition for how their systems behave and fail. When AI runs into a hard, never-seen-before incident it cannot solve, engineers will have to take over with less practice than they would have had before.

Human-factors researcher Lisanne Bainbridge described this paradox in her famous 1983 paper, The Ironies of Automation. She explained that automation reduces operators’ opportunities to practice routine work while leaving them responsible for new and abnormal situations. She argues that, therefore, operators need to be more skilled and receive even more training than before automation.

In the years to come, I predict that the average MTTR for most incidents will go down – thanks to AI-assisted incident response – but that the resolution time will shoot up for complex incidents because incident responders lost touch with their system and are struggling to investigate.

Sylvain Kalache in AI handles incidents, engineers lose touch with their systems.

Finally realized why it’s so exhausting and stressful to work with AI agents 8h a day.

When delegating a task, you want to be assured it’s off your plate and you can forget about it, knowing it will be done completely and correctly. AI doesn’t give you that: you need to check it’s doing the right thing, it’s not forgetting anything, not taking short cuts etc.

So it adds to your mental load, rather than removing from it.

Gunnar Morling, software engineer.

Armin Ronacher mostra con degli esempi come GPT-6 Astra scriva codice illegibile. "It's AGI if you don't look". Codice denso e oscuro e assenza totale di commenti. La fine di un'arte.

Il customer service completamente automatizzato con AI aumenta i rischi legati allo spoofing:

how i hacked 320+ companies that replaced their cs team with "smart" ai agents:

1. drafted a gdpr request to support@
2. changed the FROM header from my e-mail to yours (spoofing)
3. put myself in CC
4. ai agent responds with YOUR data to BOTH of us 😈

Qui il tweet e qui l'articolo.

Il nuovo modello di previsione del meteo di Google WeatherNext3 è il nuovo stato dell'arte nelle previsioni meteo e la cosa migliore è che non è solo ricerca ma finisce direttamente nei prodotti e a disposizione del pubblico:

To bring these breakthroughs out of the lab and into the real world, we’re integrating WeatherNext 3 across Google’s core ecosystem and beyond:

• High-resolution forecast data: We’re making global weather predictions, updated hourly and ready to integrate into your workflows with no model setup required. This enables researchers, developers and businesses to query the data in BigQuery and Earth Engine, or bulk-download from Google Cloud Storage.
• Available globally: WeatherNext 3 will begin powering weather experiences within Google Search, Gemini app, Google Maps, Google Maps Platform Weather API, and Google Earth Engine starting today. The update dramatically improves longer term forecasts. When planning a day or more ahead, people will see up to 50% more accurate precipitation forecasts — with the greatest improvements in regions where forecasts have historically been less reliable. So if you’re packing for a weekend trip or deciding the best day for an outdoor activity, you’ll now get more accurate predictions to help you plan.

I dati si possono consultare su mappa qui nel Weather Lab.

AI enables low-quality results, encourages lack of discipline and skill under the excuse of efficiency, it makes people use their brains less, and personally it just kills all the fun.

Ilya Miskov, designer.

Oggi viviamo in un'epoca in cui l'incubo del bambino di Rodari, la macchina per fare i compiti, è scaricabile praticamente gratis da Internet. La tecnologia digitale è evoluta in maniera talmente rapida, veloce e pervasiva che moltissime attività che ritenevamo esclusiva degli esseri umani, come studiare, conoscere, giudicare decidere, in una parola pensare, possano essere svolte da macchine. Questo vuol dire che se una macchina può sostituire il mio pensiero quella macchina prima o poi catturerà anche la mia libertà.

Per secoli abbiamo chiesto alla tecnologia di fare quello che desideravamo. Oggi le chiediamo cosa desiderare e questo crea un rischio nuovo, di dominio, di schiavitù. O una possibilità di liberazione.

Andrea Simoncini, professore ordinario di diritto costituzionale, al meeting di Rimini.

Un giorno bussò alla nostra porta uno strano tipo: un ometto buffo, vi dico, alto poco più di due fiammiferi. Aveva in spalla una borsa più grande di lui.

– Ho qui delle macchine da vendere, – disse.

– Fate vedere, – disse il babbo.

– Ecco, questa è una macchina per fare i compiti. Si schiaccia il bottoncino rosso per fare i problemi, il bottoncino giallo per svolgere i temi, il bottoncino verde per imparare la geografia: la macchina fa tutto da sola in un minuto.

– Compramela, babbo! – dissi io.

– Va bene, quanto volete?

– Non voglio denari, – disse l’omino.

– Ma non lavorerete mica per pigliar caldo!

– No, ma in cambio della macchina voglio il cervello del vostro bambino.

– Ma siete matto? – esclamò il babbo.

– State a sentire, signore, – disse l’omino, sorridendo, – se i compiti glieli fa la macchina, a che cosa gli serve il cervello?

– Comprami la macchina, babbo! – implorai. – Che cosa ne faccio del cervello?

Il babbo mi guardò un poco e poi disse:

– Va bene, prendete il suo cervello e non se ne parli più.

L'omino mi prese il cervello e se lo mise in una borsetta. Com’ero leggero, senza cervello! Tanto leggero che mi misi a volare per la stanza, e se il babbo non mi avesse afferrato in tempo sarei volato giù dalla finestra.

– Bisognerà tenerlo in gabbia, adesso – spiega l'ometto.

– Ma perché? – domandò il babbo.

– Non ha più cervello, ecco perché. Se lo lasciate andare in giro, volerà nei boschi come un uccellino, e in pochi giorni morirà di fame!

Il babbo mi rinchiuse in una gabbia, come un canarino. La gabbia era piccola, stretta, non mi potevo muovere. Le stecche mi stringevano tanto che… alla fine mi svegliai spaventato. Meno male che era stato solo un sogno! Vi assicuro che mi sono subito messo a fare i compiti.

Gianni Rodari in La macchina per fare i compiti.

AI will not replace filmmakers. That's what people are telling me. And it's what I've been trying to tell myself for a while now. The problem is that I don't think it's true. I hope I'm wrong. I hope that in 10 years we'll all look back at this video and laugh. But right now there is no one laughing. I still have to do my own dishes and laundry while AI is making art or whatever you want to call it.

What bothers me the most is that for the rest of my life whenever I see a photo, a video, or any kind of media I have to ask myself if it's real or generated. The fact that I have to ask myself that for the rest of my life.

I don't have words for that. Or I do have one word. A word that fits all of this perfectly. Dystopia.

Andreas Hem (YouTube)

Altra vittima del vibe coding, Flussonic aveva un sito così bello prima, ora è una sloppata palese:

Prima:

Claude ha iniziato a parlare difficile da aprile (Opus 4.7):

Io avevo notato il problema da Opus 4.8 come avevo scritto qui.

The fact that human touch is starting to become less and less frequent. No effort, no genuine and creative input from people. Just AI slop. Code is being entirely written by AI, designs generated by AI. Even videos are being voiced by AI these days.

Ilya Miskov, designer.

While there are massive opportunities for disruption, the SaaS business model is not going to evaporate overnight. In our view, the distinction comes down to a quick survival checklist. If you meet one of these criteria, you are likely safe. If you miss on all of them, you should be worried:

• Are you a system of record?
• Do you do more than help humans automate a single workflow?
• Are you mission-critical?

The SaaS Extinction Test

Assurda crescita dell'attività su GitHub dovuta all'AI, era già notevole ad aprile, da aprile a oggi lo è ancora di più.

Due disservizi recenti sono stati causati proprio dalla scala:

Neither outage was caused by a code or configuration change. Both incidents were capacity failures at their core. We failed to scale critical components before demand exceeded their capacity. Since April, monthly commits have grown from 1.4 billion to 2.9 billion. That growth explains the pressure on our systems, but it does not excuse these outages.

E continua la migrazione verso Azure perché la capacità dei datacenter proprietari pare essere terminata:

As part of the reliability commitments we made earlier this year, we have focused on three priorities: adding capacity, improving efficiency, and removing architectural bottlenecks. We have since added more than 3 million CPU cores, 120 petabytes of high-speed storage, and significant network capacity. We installed as much hardware as available power allowed in our existing data centers while accelerating our migration to Azure.

Today, Azure serves roughly 58% of GitHub’s platform load and half of all Git operations, up from 12% of platform load in May. This expanded footprint has also supported the growth in GitHub Actions job runs shown below.

I CTO si sono stufati di fare i CTO, scrive Gergely Orosz:

Unrealistic expectations, including about AI, by founders and CEOs are the leading cause of jobs turning bad for CTOs and VPEs right now in 2026:

• CTO expected to magically transform the company to be “AI-native”
• CTO must make significant engineering cost cuts of up to 20-50%, including morale-sapping job cuts
• “Do more with less” equals shipping more with fewer people (e.g., no backfills)
• CTO faces pressure on business results as AI coding bills rack up
• Founder slop: they want wonky AI prototypes shipped as full-blown products within weeks

I think the main reason many people (including me), very often, lack the motivation to read content that is likely generated by AI is the suspicion that it comes from a place of intellectual laziness.

afr0ck in un commento su Hacker News.

Pattern di UI per interfacce AI, beautifului.dev: